USING CA-MARKOV MODEL TO MODEL THE SPATIOTEMPORAL CHANGE OF LAND USECOVER IN FUXIAN LAKE FOR DECISION SUPPORT
S.H. Li
a,b
, B.X. Jin
b,
, X.Y. Wei
b,c
, Y.Y. Jiang
d
, J.L.Wang
a a
College of Tourism Geographic Sciences, Yunnan Normal University ，
768 Juxian Street in Chengong District, Kunming, Yunnan, China-lsh8010163.com, wang_jinlianghotmail.com
b
Yunnan Provincial Geomatics Centre, 404 West Ring Road, Kunming, Yunnan, China- lsh8010, jinbx163163.com, 19423221qq.com
c
College of Geographic Sciences, Nanjing Normal University, No.1,Wenyuan Road ，
Xianlin University District,Nanjing,China- 19423221qq.com
d
Center for Intelligent Spatial Computing, George Mason University, 4400 University Dr., Fairfax, VA, USA-yjiang8gmu.edu
KEY WORDS: LUCC, CA-Markov Model, Dynamic Modelling, Optimized Modelling Scale Combination, Fuxian Lake Watershed
ABSTRACT: Spatiotemporal modelling of land usecover change LUCC has become increasingly important in recent years, especially for
environmental change and regional planning. There have been many approaches and software packages for modelling LUCC, but developing a model for a specific region is still a difficult task, because it requires large volume of data input and elaborate model
adjustment. Fuxian Lake watershed is one of the most important ecological protection area in China and located in southeast of Kunming city, Yunnan province. In this paper, the CA-Markov model is used to analyse the spatiotemporal LUCC and project its
course into the future. Specifically, the model uses high resolution remote sensing images of 2006 and 2009 as input data, and then makes prediction for 2014. A quantitative comparison with remote sensing images of 2014 suggests an overall accuracy of 88. This
spatiotemporal modelling method is expected to facilitate the research of many land cover and use applications modelling.
Corresponding author
1. INTRODUCTION
Since LUCC has direct and indirect impact on a number of factors of ecological environment, as well as the regional and
global sustainable development, the land change modelling has attracted increasing attention in the context global climate
change Li, 1996; Wijesekara et al., 2012. The continuous evolution and transformation of land surface has resulted in
serious consequence to the physical system at multiple scales, and raised a number of change in the ecological processes, such
as surface runoff, soil erosion and agricultural non-point source pollution Wijesekara et al., 2012; Li et al., 2010; Ouyang et al.,
2010. Analysing the characteristics of the LUCC, exploring changes at different spatiotemporal scale and predicting future
scenarios contribute is of significance for providing decision- making basis for the regional ecological protection and
sustainable development. At present, mainstream LUCC models include modelling system
dynamics model, Clue-S model, Multi-agent model and Markov model Duan et al., 2004; Wang et al., 2012; Xiao et al., 2012;
Yang et al., 2007; Qin et al., 2009; Hou et al., 2004. None of them are perfect. The efficiency of Clue-S model is not
satisfactory and it has to rely on results from other auxiliary software. The Markov model can quantitatively predict the
dynamic changes of landscape pattern, while it cant deal with the spatial pattern of landscape change Balzter et al., 1998. In
contrast, the cellular automata CA model is able to predict the spatial distribution of the landscape pattern
， but it cannot
predict the change in time dimension Cheng et al., 2013. In this context, researchers have turned to integrating different
methods to study dynamic modelling of LUCC Qin et al., 2009.
Among them, CA-Markov that combines CA with Markov integrates the advantages of both methods. Since it is able to
model the spatiotemporal dynamic change of land cover change, the model is widely applied in many scientific communities.
Balzter et al. 1998 simulated the spatial dynamic change process of vegetation in Giessen University in German from
1993 to1996. Liu and Andersson 2004 simulated the evolution of the settlement pattern of two cities. Jenerette and
Wu 2001 analysed and simulated land use change situation of Phoenix district in Arizona in the United States, the results
show that land use change is closely related to the urban expansion and population increase in the past 83 years.
Although the research of using CA model to simulate LUCC mainly focuses on applied research, the sensitivity of the model
with different parameters is also needed to be analysed. Berling and Wu 2004
used multi-scale testing method to calibrate and validate the model of Phoenix town development, and it is
concluded that the higher spatial resolution of the input data was, the higher accuracy of modelling using the CA model
would be. Menard and Marceau 2005 studied the sensitivity of CA model between the neighbour structure and the spatial
resolution in 2005. Mondal et al. 2012 revealed that the 5x5
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper. doi:10.5194isprsannals-II-4-W2-163-2015
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contiguity filters produced most geospatially distributed effective results based on a comparison with different contiguity
filters i.e. 3 x 3, 5 x 5 and 7 x 7 contiguity filters. Liu et al. 2004 explored impact from the time interval the on urban
development model based on CA. Verda et al. 2006 discussed the sensitivity of the neighbour structure to the model.
Taking urban growth model of Changsha city as an example, Yin et al. 2008 studied the problem of the modelling scale
using CA model, and presented that city growth model with CA has higher modelling accuracy only in certain scale, and the
model has a certain sensitivity to the scaleLi and Liu, 2007. Ke et al. 2010 has looked into the influence of cellular size to
cellular automata model. The research mentioned above is focused on the sensitivity of a single scale, such as cellular size
and scope of neighbourhood. A lot of work about the scale
’s relationship with itself and the optimal combination of various
scale for higher modelling precision remained to be done. To address these challenges, taking Fuxian Lake watershed as
study area, the relationship between cellular size and neighbourhood range is analysed. In addition, its impact on
prediction accuracy in a predetermined time interval is discussed.
2. DATA AND STUDY AREA
